att_abstract={{The field of computer vision has experienced rapid growth over the
past fifty years.  Many computer vision problems have been solved
using theory and ideas from algebraic projective geometry. In this
research, we look at a previously unsolved problem from object
recognition, namely object recognition when the correspondences
between the object and image data is not known a priori. We
formulate this problem as a mixed-integer nonlinear optimization
problem in terms of the unknown projection relating the object and
image, as well as the unknown assignments of object points and lines
to those in the image.  The global optimum of this problem recovers
the relationship between the object points and lines with those in
the image. When certain assumptions are enforced on the allowable
projections mapping the object into the image, a proof is provided
which permits one to solve the optimization problem via a simple
decomposition. We illustrate this decomposition approach on some
example scenarios.
	att_categories={C_CCF.2, C_CCF.1, C_CCF.7, C_CCF.8, C_IIS.7},
	att_copyright_notice={{The definitive version was published in International Transactions in Operational Research. {{, Volume 18}}{{, Issue 4}}{{, 2011-07-01}}}},
	att_tags={object recognition,  point/line projection,  GRASP,  continuous GRASP,  global optimization,  stochastic local search,  nonlinear programming},
	author={Mauricio Resende and M.J. Hirsch, Raytheon and P.M. Pardalos},
	institution={{International Transactions in Operational Research}},
	title={{Correspondence of projected 3D points and lines using a
continuous GRASP